Method of separating front view and background and apparatus

ABSTRACT

A method of initially estimating a front view portion of a photographed image and separating the photographed image into a front view and a background without user interaction and apparatus performing the method are provided. The method of separating a front view and a background of an image includes dividing one or more pixels included in a photographed image into pixel groups according to color similarity between the pixels, estimating the position of the front view in the image divided into the pixel groups, and separating the front view and the background based on the estimated position of the front view. The method automatically separates the front view and the background of the image without a user input.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is related to and claims priority to Korean PatentApplication No. 10-2010-130839, filed on Dec. 20, 2010 in the KoreanIntellectual Property Office, the disclosure of which is incorporatedherein by reference.

BACKGROUND

1. Field

The embodiments discussed herein are related to a method of separating afront view and a background of a photographed image.

2. Description of the Related Art

When a robot recognizes an object or a person in an indoor or outdoorenvironment, a technique for separating an input image into a front viewand a background is important. In a recognition technique, an object maybe erroneously recognized due to a feature point extracted from a placeother than an object. Even when using a boundary-based recognitionalgorithm, a recognition rate is lowered due to misjudgment of an objectboundary. Accordingly, accurate separation of an image into a front viewand a background is important in a robot recognition technique.

There are various methods of separating a front view and a background ofan image. A method of expressing an image using a Markov Random Field(MRF) model and minimizing energy using graph cut or Belief Propagation(BP) is generally used. To accurately separate a front view and abackground, a predefined front view region and a predefined backgroundregion are necessary.

Algorithmic results for separating a front view and a background of therelated art is not completely automated. A front view region and abackground region are set by a user and the front view and thebackground of the image are separated based on the set regions.

In addition, in the algorithm for separating the front view and thebackground in the related art, recognition rate may be lowered due tobackground noise, edges, or patterns within the image.

SUMMARY

An aspect of an exemplary embodiment discussed herein relate toproviding a method of initially estimating a front view portion from aphotographed image and separating the photographed image into a frontview and a background based on the front view portion without userinteraction.

Additional aspects of the invention will be set forth in part in thedescription which follows and, in part, will be obvious from thedescription, or may be learned by practice of the invention.

In accordance with an aspect of an exemplary embodiment, a method isprovided of separating a front view and a background of an imageincluding dividing one or more pixels included in a photographed imageinto pixel groups according to color similarity between the pixels,estimating the position of the front view in the image divided into thepixel groups, and separating the front view and the background based onthe estimated position of the front view.

The dividing of one or more pixels included in a photographed image intothe pixel groups according to the color similarity between the pixelsmay include converting an RGB color space of the photographed image intoa Commission internationale de l'eclairage (CIE) Lab color space.

The dividing of one or more pixels included in a photographed image intothe pixel groups according to the color similarity between the pixelsmay include calculating a color distance between any one pixel (firstpixel) of the image converted into the CIE Lab color space and anadjacent pixel (second pixel) of the first pixel, changing the color ofthe second pixel to the color of the first pixel if the color distanceis less than a threshold, and forming the pixel groups.

The dividing of one or more pixels included in a photographed image intothe pixel groups according to the color similarity between the pixelsmay include calculating a color distance between the second pixel and anadjacent pixel (third pixel) of the second pixel if the color distancebetween the first pixel and the second pixel is greater than or equal tothe threshold, and changing the color of the third pixel to the color ofthe second pixel if the calculated color difference is less than thethreshold.

The estimating of the position of the front view in the image dividedinto the pixel groups may include detecting edges between the pixelgroups from the image divided into the pixel groups and generating anedge image, detecting nodes where two or more edges meet from thegenerated edge image, calculating a circular score indicating similaritybetween a start node and other nodes in a circular path which startsfrom any one of the nodes, passes through the edges and the other nodesand arrives at the start node, and estimating a circular center having ahighest circular score among the calculated circular scores as thecenter position of the front view.

The generating of the edge image may include generating the edge imageusing a canny edge detector.

Each node where two or more edges meet may be a center pixel of an n×nwindow when the number of pixel groups included in the n×n window isthree or more while moving the n×n window from a start pixel to a lastpixel of the image.

The calculating of the circular score may include generating atwo-dimensional color histogram based on an a* axis and a b* axis of theCIE Lab color space of pixels belonging to the pixel groups forming eachnode, comparing the two-dimensional color histograms of pixel groups oftwo nodes having three or more pixel groups and calculating a largesttwo-dimensional color histogram as a histogram similarity, andcalculating the circular score of the circular path based on thecalculated histogram similarity.

The calculating of the circular score of the circular path may includecalculating the circular score such that an edge passing through thecircular path is not selected more than once.

The comparing of the histograms may be performed based on a chi squaretest.

BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed incolor. Copies of this patent or patent application publication withcolor drawing(s) will be provided by the Office upon request and paymentof the necessary fee. These and/or other aspects of the invention willbecome apparent and more readily appreciated from the followingdescription of the embodiments, taken in conjunction with theaccompanying drawings of which:

FIG. 1 illustrates a system for separating a front view and a backgroundof a photographed image according to an embodiment of the presentinvention;

FIG. 2 illustrates a method of separating a front view and a backgroundof an image according to an embodiment of the present invention;

FIG. 3 illustrates a process of dividing an image into pixel groupsaccording to an embodiment of the present invention;

FIG. 4 illustrates an exemplary photographed image divided into pixelgroups according to another embodiment of the present invention;

FIG. 5 illustrates a process of estimating the position of a front viewwithin an image according to another embodiment of the presentinvention;

FIG. 6 illustrates an edge image generated by detecting an edge of animage divided into pixel groups according to another embodiment of thepresent invention;

FIG. 7 illustrates a window including a node according to anotherembodiment of the present invention;

FIG. 8 an edge image including a node;

FIG. 9 illustrates a histogram of any one pixel group included in awindow;

FIG. 10 illustrates an operation for calculating a circular scoreaccording to another embodiment of the present invention;

FIG. 11 illustrates an estimation of the position of a front view of animage according to another embodiment of the present invention;

FIG. 12 illustrates a designation of the position of a front view of animage estimated according to another embodiment of the presentinvention; and

FIG. 13 illustrates a separation of a front view and a background of animage according to another embodiment of the present invention.

DETAILED DESCRIPTION

Reference will now be made in detail to the embodiments of the presentinvention, examples of which are illustrated in the accompanyingdrawings, wherein like reference numerals refer to like elementsthroughout.

FIG. 1 illustrates a system for separating a front view and a backgroundof a photographed image according to an embodiment of the presentinvention.

The system for separating the front view and the background of the imageincludes a photographing unit 30, an image processor 40, a front viewestimator 50, an image separator 60 and a display unit 70.

The photographing unit 30 photographs an object and acquires an image.The photographing unit 30 may include a camera and a video camera.

The image processor 40 processes the image acquired by the photographingunit 30, for ease of front view estimation. The image processor 40generates pixel groups based on a color difference between one or morepixels forming the image. Pixels having similar colors may be treated asone group.

The front view estimator 50 distinguishes between the front view and thebackground based on the pixel groups generated by the image processor 40and estimates the front view.

The image separator 60 separates the front view estimated by the frontview estimator 50 from the background of the image.

The display unit 70 displays the front view separated by the imageseparator 60.

FIG. 2 illustrates a method of separating a front view and a backgroundof an image according to another embodiment of the present invention.

One or more pixels included in a photographed image are divided intopixel groups according to color similarity between pixels (operation 100The position of a front view of the image divided into the pixel groupsmay be estimated (operation 200). The front view and the background ofthe image are separated based on the estimated position of the frontview within the image.

Division of one or more pixels included in the photographed image intopixel groups according to color similarity between the pixels isdisclosed with reference to FIG. 3.

FIG. 3 illustrates a process of dividing an image into pixel groupsaccording to an exemplary embodiment of the present invention.

One or more pixels included in a photographed image may have differentcolor values. If a front view and a background of an image are separatedusing the photographed image, a node and an edge of the image areincreased in proportion to the size of the image. That is, if thephotographed image is used, error occurs due to image noise or acalculation time required for separating a front view and a backgroundof an image may be increased.

Therefore, the photographed image may be divided into pixel groups asillustrated in FIG. 3.

The photographed image is input (operation 101).

The input image may have a RGB color space. In the RGB color space, onlythree wavelengths of R (red), G (green) and B (blue) may bequantitatively expressed and brightness, chromaticity and color of theimage may not be specifically expressed.

Therefore, the RGB color space may be converted into a CIE Lab (CIEL*a*b) color space (102).

The CIE Lab color space is a color model similar to how humans recognizecolor and may express the color in a two-dimensional matrix based on anL* channel indicating brightness, an a* channel indicating color, and ab* channel indicating chromaticity. The input RGB image may be convertedinto the CIE Lab color space using Equation 1 and Equation 2.

$\begin{matrix}{\begin{bmatrix}X \\Y \\Z\end{bmatrix} = {\begin{bmatrix}0.412453 & 0.357580 & 0.180423 \\0.212671 & 0.715160 & 0.072169 \\0.019334 & 0.119193 & 0.950227\end{bmatrix}\begin{bmatrix}R \\G \\B\end{bmatrix}}} & {{Equation}\mspace{14mu} 1} \\{\begin{matrix}{X = {X/{Xn}}} & ( {{Xn} = 0.950456} ) \\{Z = {Z/{Zn}}} & ( {{Zn} = 1.088754} ) \\{L = {116Y^{\frac{1}{3}}}} & ( {Y > 0.008856} ) \\{L = {903.3Y}} & ( {Y<=0.008856} )\end{matrix}{a = {{500( {{f(X)} - {f(Y)}} )} + {delta}}}{b = {{500( {{f(X)} - {f(Z)}} )} + {delta}}}\begin{matrix}{{f(t)} = t^{\frac{1}{3}}} & ( {t > 0.008856} ) \\{{f(t)} = \frac{{7.787t} + 16}{116}} & ( {t < 0.008856} )_{|}\end{matrix}} & {{Equation}\mspace{14mu} 2}\end{matrix}$

Equation 1 denotes an equation for converting the values of the RGBcolor space of a pixel into X, Y and Z values and Equation 2 denotes anequation for converting the X, Y and Z values into L, a and b values.According to an exemplary embodiment, the L, a and b values are equal tothe L*, a* and b* values of the CIE L*a*b color space.

An iterative number may be set to 0, to initialize all pixels of theimage before search (operation 103).

Next, a pixel to be searched may be set (operation 104).

After the pixel to be searched is set, the set pixel may be comparedwith upper, lower, left and right pixels adjacent to the set pixel(operation 105).

Comparison may be performed based upon a color distance between pixels(operation 106).

If the color distance is less than a predetermined threshold, it isdetermined that the color distance between both pixels falls within anerror range and the color value of the current reference pixel is copiedto the compared pixel (operation 107). That is, both pixels may betreated as one pixel (pixel group) having the same color value.

After the color value is copied, the compared pixel may be redefined asthe search pixel (operation 108). Thereafter, the redefined search pixelis compared with upper, lower, left and right pixels adjacent to thesearch pixel (operation 105).

If the color distance is greater than or equal to the predeterminedthreshold, it is determined whether the compared pixel is a last searchpixel (operation 109).

If it is determined that the compared pixel is the last search pixel, itis determined whether the iterative number is greater than apredetermined value n (operation 110), to reduce error. The iterativenumber may be set according to an error range desired by a user.

If the iterative number is greater than the predetermined value, animage segmentation process is finished. In contrast, if the iterativenumber is less than or equal to the predetermined number, it isdetermined that search is not yet finished. Then, the iterative numberis increased (operation 112) and search is performed again (operation104).

After the input image is converted into the Lab color space, the colordistance between adjacent pixels may be iteratively calculated whilesearching for first to final pixels of the image. If the color distancefalls within a predetermined range, the colors of the compared twopixels are changed to the same color. The color distance between the twopixels may be obtained by Equation 3.

$\begin{matrix}{{distance} = \sqrt{\begin{matrix}( {( {{L( {x,y} )} - {L( {{x + u},{y + v}} )}} )^{2} + ( {{a( {x,y} )} -} }  \\ { {a( {{x + u},{y + v}} )} )^{2} + ( {{b( {x,y} )} - {b( {{x + u},{y + v}} )}} )^{2}} )\end{matrix}}} & {{Equation}\mspace{14mu} 3}\end{matrix}$

X(x, y) denotes an X value (L, a or b) of a pixel (x, y) and X(x+u, y+v)denotes an X value of an adjacent pixel of the pixel. In addition, u andv generally have a value between −1 and 1.

If the color distance is less than the predetermined threshold, aprocess of setting the adjacent pixel to the search pixel, obtaining thecolor distance between the search pixel and the adjacent pixel thereofand changing the color value of the adjacent pixel to the same colorvalue is iteratively performed. By iteratively performing such aprocess, an image divided into pixel groups may be finally obtained.

FIG. 4 illustrates a photographed image divided into pixel groupsaccording to an exemplary embodiment of the present invention.

Image 400 is an original input image and image 410 is a photographedimage divided into pixel groups. That is, the photographed image dividedinto the pixel groups may be simplified as compared to the originalimage.

Estimation of the position of the front view based on the photographedimage divided into the pixel groups is disclosed with reference to FIG.5.

FIG. 5 illustrates a process of estimating the position of a front viewwithin an image according to an embodiment of the present invention.

Estimation of the position of the front view in the image divided intothe pixel groups includes detecting an edge, graphing pixels included inthe image (generating a node 10 and generating a segment histogram), andcalculating a circular score of the node 10.

Image edges may be detected from the image divided into the pixel groups(operation 210). Examples of the edge detection method include a methodof detecting an edge using a canny edge detector.

In general, the edge detector is sensitive to noise. The canny edgedetector is an algorithm which has been developed to prevent incorrectedge detection from being performed due to noise.

The algorithm of the canny edge detector provides a method of finding anedge satisfying good detection, good localization and clear response.

Good detection may be defined as a capability to detect all actual edgesand good localization minimizes a difference between an actual edge anda detected edge. In addition, clear response may be defined as a uniqueresponse of each edge.

The algorithm of the canny edge detector includes calculating a gradientin x and y directions through Gaussian smoothing filtering, calculatingedge strength based on the gradient, and utilizing hysteresis.

FIG. 6 illustrates an edge image generated by detecting an edge of animage divided into pixel groups according to another embodiment of thepresent invention.

The image is graphed using the detected edges (operation 220). Thegraphing operation includes generating a node 10 and generating ahistogram of each pixel.

In the generation of the node 10, the node 10 is a point where two ormore edges meet in the edge image. While an n×n window 5 is moved from astart pixel to a last pixel of the image, the color of each pixel of thewindow 5 is determined and a determination as to how many pixel groupsthe window 5 is divided into is made. If the number of pixel groups isthree or more, it may be determined that the pixel of the window is thenode 10.

FIG. 7 illustrates a window including a node according to anotherembodiment of the present invention. As illustrated in FIG. 7, since thewindow includes three pixel groups S1, S2 and S3, the window includesthe node 10. FIG. 8 is a schematic view of the edge image of FIG. 6including the node of FIG. 7.

An operation of generating the histogram of the pixel is disclosed.

Each node 10 may have connected edges and a pixel group. An edge has anode 10 or a point of an image other than the node at both ends thereof.The pixel group included in the node 10 includes values obtained byconverting the pixels belonging to the pixel group into a CIE L*a*b*color space and, based on these values, a two-dimensional histogrambased on an a* axis and a b* axis may be generated. The a* axis and theb* axis may have various sizes and may have, for example, a bin having asize of 32. That is, a and b values of each bin are illustrated in Table1.

TABLE 1 bin a* b* 1 −128~−96  −128~−96  2 −96~−64 −96~−64 3 −64~−32−64~−32 4 −32~0    −32~0    5  0~32  0~32 6 32~64 32~64 7 64~96 64~96 8 96~128  96~128

The two-dimensional histogram of each pixel is illustrated in FIG. 9.FIG. 9 is a schematic diagram showing a histogram of any one pixelgroup, for example, S3 included in the window illustrated in FIG. 7.

The number of the node 10 may be initialized. The number of the node 10may be set to 1 (operation 230).

A circular score is calculated (operation 240). The circular score is acriterion used when the front view of the image is obtained. A circularcenter having a highest score may be determined to be a front view of anobject.

The circular score is a numerical value indicating similarity betweennodes 10 in a circular path which starts from one node 10 (the number 1of the node 10), passes through edges and other nodes 10 and returns tothe node 10. Objects within an image generally have closed curves andthe color distribution of the closed curves is similar. Accordingly, ifcirculation is performed along edges configuring the closed curve of theobject, a highest circular score is obtained.

The process of acquiring the circular score may be performed by theprocess illustrated in FIG. 10 and a state diagram illustrated in FIG.11.

FIG. 10 illustrates a calculating a circular score according to anexemplary embodiment of the present invention, and FIG. 11 illustratesan estimation of the position of a front view of an image according toan exemplary embodiment of the present invention.

Referring to FIG. 10, a node (A) is selected (operation 241), an edge ofthe current node (A) is selected, and an opposite node (B) is selected(operation 242).

A determination is made as to whether the selected node (A) and node (B)are the same or whether the selected node (A) or node (B) are a point ofan image other than the node (operation 243).

If it is determined that the selected node (A) and node (B) are the sameor the selected node (A) or node (B) are a point of an image other thanthe node, it is determined whether all nodes have been searched(operation 244). If it is determined that all nodes have been searched,the operation for calculating the circular score is finished and, if itis determined that all nodes have not been searched, Operation 241 forselecting the node is performed again.

If it is determined that the selected node (A) and node (B) are not thesame or the selected node (A) or node (B) are not an end of the image,an operation for calculating histograms of the node (A) and the node(B), calculating histogram similarity based on the histograms, andadding the histogram similarity (that is, accumulating the histogramvalue) (operation 245), and then Operation 242 of selecting the node areperformed. That is, the histogram similarity is obtained with respect toall nodes and is accumulated, thereby calculating the circular score.

The histogram similarity between the node A and the node B is measuredby comparing the color histograms of the pixels included in the pixelgroups of the node. The node has three or more pixel groups and ahighest value obtained by comparing all the pixel groups of two nodes isset to a similarity between the pixel groups of the two nodes. Thecomparison between the histograms is performed using a chi square test.The histogram similarity S between the two nodes (A, Bi) is obtained byEquations 4 and 5.

$\begin{matrix}{{S( {A,B_{i}} )} = {\arg\;\max\;{S( {a,b_{i}} )}}} & {{Equation}\mspace{14mu} 4} \\{{S( {a,b_{i}} )} = {{\frac{1}{2}{\sum\limits_{k = 1}^{K}\frac{\lbrack {{h_{a}(k)} - {h_{b_{i}}(k)}} \rbrack^{2}}{{h_{a}(k)} + {h_{b_{i}}(k)}}}} - D}} & {{Equation}\mspace{14mu} 5}\end{matrix}$where, a and b respectively denote pixel groups respectively belongingto nodes (A and B) and h_(a) and h_(b) respectively denote thehistograms of the pixel groups. In addition, i denotes the number ofnodes which has passed up to now or the index of a current node incirculation. D denotes a reference value of the histogram similarity.The histogram similarity may be expressed as a numerical value based onthe value D.

A circular score CS of circulation j which starts from a node (a) andpasses through k nodes 10 may be expressed by a histogram similarity sumand is expressed by Equation 6.

$\begin{matrix}{{{CS}(j)} = {\sum\limits_{i = 0}^{k}{S( {a,b_{i}} )}}} & {{Equation}\mspace{14mu} 6}\end{matrix}$

A circular center P in which the calculated histogram similarity sum ishighest may be estimated as the position of the front view of the image.The circular center P may be obtained by Equation 7.

$\begin{matrix}{{{p(j)} = ( {x,y} )}{x = \frac{\sum\limits_{i = 1}^{k}x_{i}}{k}}{y = \frac{\sum\limits_{i = 1}^{k}j_{i}}{k}}} & {{Equation}\mspace{14mu} 7}\end{matrix}$

As illustrated in FIG. 7, the circular center P corresponds to anaverage of the positions of all the passing nodes 10 during circulation.

FIG. 11 illustrates the circular score exists in all nodes 10 and allcirculations. One node may be set to a reference node (N1). The currentnode (N1) may be connected to two or more edges (an edge E1 and an edgeE6). One edge may be selected from the two edges and a node which islocated at the opposite side of the selected edge is selected. Thehistogram similarity E1 between the reference node (N1) and the currentnode (N2) is measured and is added to the circular score.

One edge is selected from among the edges connected to the current node(N2), a node (N3) located at the opposite side of the selected edge isselected, and the histogram similarity between the reference node (N2)and the current node (N3) is measured and is added to the circularscore. When an edge is selected in the node, an edge which has alreadybeen used may not be selected regardless of directions.

Such a process is finished when an end of the image is reached and thecurrent node is returned, and the score acquired by this process and thecenter value of the positions of the passing nodes are stored. In FIG.11, the center position of the current circulation is P. After thisprocess is finished with respect to all nodes, the acquired scores arecompared and the center position of a node having a maximum circularscore is the position of the front view of the object within the image.

A determination is made as to whether the number of the selected node 10is greater than or equal to the total number of nodes (operation 250).

If the number of the selected node is greater than or equal to the totalnumber of nodes, since the circular scores of all the nodes have beencalculated, the circular center value P having the maximum circularscore is estimated as the position of the front view within the image(operation 260).

If the number of the selected node is less than the total number ofnodes, since all nodes have not been searched, the number of the node isincreased, the non-searched nodes are searched and the circular score iscalculated again.

FIG. 12 illustrates a designation of the position of a front view of animage estimated according to an embodiment of the present invention, andFIG. 13 illustrates a separation of a front view and a background of animage according to an embodiment of the present invention.

To separate a front view from a background of an image, graph cut may beused. If an average position of a node of a circular center P having ahighest circular score is designated as a front view of graph cut (see,for example, FIG. 12) and a boundary is designated as a background so asto perform graph cut (see, for example, FIG. 13), it is possible toautomatically separate the front view and the background of the imagewithout user input.

According to an exemplary embodiment of a method of separating the frontview and the background of the image, it is possible to automaticallyseparate the front view and the background of the image and improveperformance of an algorithm for recognizing an object or the attitude ofan object.

Although a few embodiments of the present invention have been shown anddescribed, it would be appreciated by those skilled in the art thatchanges may be made in these embodiments without departing from theprinciples and spirit of the invention, the scope of which is defined inthe claims and their equivalents.

What is claimed is:
 1. A method of separating a front view and abackground of an image, the method comprising: dividing with a processorone or more pixels included in a photographed image into pixel groupsaccording to color similarity between the pixels; estimating theposition of the front view in the image divided into the pixel groups;and separating the front view and the background based on the estimatedposition of the front view, wherein the estimating of the position ofthe front view in the image divided into the pixel groups includes:detecting edges between the pixel groups from the image divided into thepixel groups and generating an edge image, detecting nodes where two ormore edges meet from the generated edge image, calculating a circularscore indicating similarity between a start node and other nodes in acircular path that starts from any one of the nodes, passes through theedges and the other nodes and arrives at the start node, and estimatinga circular center having a highest circular score among the calculatedcircular scores as the center position of the front view.
 2. The methodaccording to claim 1, wherein the dividing of one or more pixelsincluded in a photographed image into the pixel groups according to thecolor similarity between the pixels includes converting an RGB colorspace of the photographed image into a Commission internationale del'eclairage (CIE) Lab color space.
 3. The method according to claim 2,wherein the dividing of one or more pixels included in a photographedimage into the pixel groups according to the color similarity betweenthe pixels includes calculating a color distance between any one pixel(first pixel) of the image converted into the CIE Lab color space and anadjacent pixel (second pixel) of the first pixel, changing the color ofthe second pixel to the color of the first pixel if the color distanceis less than a threshold, and forming the pixel groups.
 4. The methodaccording to claim 3, wherein the dividing of one or more pixelsincluded in a photographed image into the pixel groups according to thecolor similarity between the pixels includes calculating a colordistance between the second pixel and an adjacent pixel (third pixel) ofthe second pixel if the color distance between the first pixel and thesecond pixel is greater than or equal to the threshold, and changing thecolor of the third pixel to the color of the second pixel if thecalculated color difference is less than the threshold.
 5. The methodaccording to claim 1, wherein the generating of the edge image includesgenerating the edge image using a canny edge detector.
 6. The methodaccording to claim 5, wherein each node where two or more edges meet isa center pixel of an n×n window when the number of pixel groups includedin the n×n window is three or more while moving the n×n window from astart pixel to a last pixel of the image.
 7. The method according toclaim 6, wherein the calculating of the circular score includes:generating a two-dimensional color histogram based on an a* axis and ab* axis of the CIE Lab color space of pixels belonging to the pixelgroups forming each node; comparing the two-dimensional color histogramsof pixel groups of two nodes having three or more pixel groups andcalculating a largest two-dimensional color histogram as a histogramsimilarity; and calculating the circular score of the circular pathbased on the calculated histogram similarity.
 8. The method according toclaim 7, wherein the calculating of the circular score of the circularpath includes calculating the circular score such that an edge passingthrough the circular path is not selected more than once.
 9. The methodaccording to claim 8, wherein the comparing of the histograms isperformed based on a chi square test.
 10. An apparatus for separating afront view and a background of an image, the apparatus comprising: animage processor dividing one or more pixels included in a photographedimage into pixel groups according to color similarity between thepixels; a front view estimator for estimating the position of the frontview in the image divided into the pixel groups; and an image separatorfor separating the front view and the background based on the estimatedposition of the front view, wherein the front view estimator detectsedges between the pixel groups from the image divided into the pixelgroups and generates an edge image, detects nodes where two or moreedges meet from the generated edge image, calculates a circular scoreindicating similarity between a start node and other nodes in a circularpath that starts from any one of the nodes, passes through the edges andthe other nodes and arrives at the start node, and estimates a circularcenter having a highest circular score among the calculated circularscores as the center position of the front view.